用系统科学和智能方法研究城市发展问题

Research on Urban Development Problem by System Science and Intelligent Methods

我国正处于城镇化的快速发展阶段.然而,在城镇化的过程中,决策者常常面临这样的问题:一个城市的资源能支撑多大的人口规模?对产业结构进行怎样的调整才能最大化释放人口承载力?下一年度的城市国内生产总值(Gross domestic product,GDP)增幅定为多少合理?GDP增幅与居民消费价格指数(Consumer price index,CPI)、就业的定量关系如何?财政投资应投向哪些领域才能最大程度地提高居民满意度?哪些决策影响城市协调发展?等等.如何在城镇化的大背景下化解上述难题,做好城市发展的顶层规划与设计,是一类重大而复杂的难题.针对此类问题,本文采用系统科学和智能方法,定性定量相结合,首先建立城市GDP、财政收入等关键指标的智能预报模型以及GDP增幅与CPI、就业的定量关系模型,在此基础上建立城市人口承载力与城市协调发展的优化决策模型,并针对我国某大型城市的若干重要决策场景进行了案例分析.结果表明,采用系统科学与智能方法,是研究和解决该类难题的新颖且行之有效的方法,相关成果有助于城市决策者提高决策水平,实现定性定量相结合的科学决策.

China is in the stage of rapid development of urbanization. However, policy makers often face such problems in the urbanization process： What is the maximal population scale that the resources of a city can support？ How to adjust the industrial structure in order to maximize the population carrying capacity？ What is the reasonable setting value of gross domestic product （GDP） growth for the next year？ What is the quantitative relationship between GDP growth and consumer price index （CPI）, employment？ Which areas should be invested in by financial investment in order to maximize resident satisfaction？ What decisions affect the coordinated development of a city？ And so on. How to meet these problems in the context of urbanization, and how to make good top-level plannings and designs of urban development are major and complex problems. For such problems, by using system science and intelligent methods combing qualitative and quantitative analysis, we first construct an intelligent forecasting model of key indexes of urban development such as GDP growth and government revenue, also we investigate a quantitative relationship model between GDP growth and CPI, Employment. Then we construct the optimization model for improving the urban population carrying capacity and the coordinated development of the city. We also make an extensive case study for some important decision scenarios of a large city. The results show that the proposed method is a novel and effective one which can be used to study and solve such problems, and that the experimental results may contribute to improving the decision making level of urban policy-makers, achieving scientific decision-making qualitatively and quantitatively.